Extended Hybrid Genetic Algorithm for Solving Job Shop Scheduling Problem

Eshim, Sumaia E.; M.Hamed, Mohammed

doi:10.7753/ijsea1002.1001

Cited by 1 publication

(2 citation statements)

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“…Similarly, Constraints (10) and (11) ensure that the completion time of each operation is equal to the completion time of the batch that processes it.…”

Section: Min C Maxmentioning

confidence: 99%

“…Kurdi [10] proposed an improved genetic algorithm (GA) for solving a JSP, where different subpopulations have different evolutionary methods, so that the individuals with the worst environmental adaptability could be exchanged to diversify the search process. To integrate the advantages of various algorithms, some researchers hybridized several algorithms to construct more effective hybrid algorithms to solve JSPs, such as the hybrid GA [11] and the hybrid tabu search-path relinking algorithm [12]. In some cases, there are certain objectives and constraints that are considered in terms of preference, and researchers have found different methods to somehow satisfy them.…”

Section: Introduction 1job-shop Scheduling Problemmentioning

confidence: 99%

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Mathematical Modeling and A Novel Heuristic Method for Flexible Job-Shop Batch Scheduling Problem with Incompatible Jobs

Zhang

et al. 2023

Sustainability

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This paper investigates a novel flexible job-shop scheduling problem, where the machines have batch-processing capacity, but incompatible jobs cannot be processed in a batch (FJSPBI) simultaneously. This problem has wide applications in discrete manufacturing, especially in chemical and steel casting industries. For the first time, in this study, a 3-indexed mixed-integer linear programming (MILP) model is proposed, which can be efficiently and optimally solved by commercial solvers for small-scale problems. In addition, an improved large neighborhood search (LNS) algorithmic framework with an optimal insertion and tabu-based components (LNSIT) is proposed, which can achieve high-quality solutions for a large-scale FJSPBI in a reasonable time. A perturbation strategy and an optimal insertion strategy are then additionally embedded to improve the exploitation and exploration ability of the algorithm. The proposed model and algorithm are tested on numerous existing benchmark instances without the incompatibility characteristics, and on newly generated instances of the FJSPBI. The experimental results indicate the effectiveness of the proposed MILP model and the algorithm, including the proposed strategies, and the optimal insertion strategy can significantly reduce the computational burden of the LNS algorithm. The comparison results further verify that the proposed LNSIT can directly solve the specific flexible job-shop batch scheduling problem without incompatibility, with better results than existing methods, especially for large-scale instances. Additionally, the impacts of a wide range of characteristics, including batch capacity, incompatibility rate, instance scale, and machine processing rate, on the performance of the LNSIT and the scheduling results are analyzed and presented.

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“…Similarly, Constraints (10) and (11) ensure that the completion time of each operation is equal to the completion time of the batch that processes it.…”

Section: Min C Maxmentioning

confidence: 99%